Neural network for processing arrays of data with existent...

Data processing: artificial intelligence – Neural network – Learning task

Reexamination Certificate

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C706S048000, C706S924000

Reexamination Certificate

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07877342

ABSTRACT:
A neural network for processing arrays of data with pertinent topology includes a n-dimensional array of cells (Ki) corresponding to the knots of the neural network, each cell having connections to the directly adjacent cells (Kj) forming the neighborhood of a cell (Ki), Each cell (Ki) has inputs for each connection to directly adjacent cells; an output for the connection to one or more of the directly adjacent cells (Kj), the connection between the cells being determined by weights (wij), and each cell being characterized by an internal value and being able to carry out signal processing for generating a cell output signal (ui), The output signal (ui) of a cell (Ki) is a function of its internal value and of the input signals from the neighboring cells, each cell being associated univocally to a record of a n-dimensional database (Pi) with pertinent topology and the value of each data record being the starting value of the corresponding cell. Processing is carried out by considering the internal value or the output value (ui) of each cell (Ki) after a certain number of iterative processing steps of the neural network as the new obtained value (Ui) for the univocally associated data records (Pi).

REFERENCES:
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patent: 6011295 (2000-01-01), Merrill et al.
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patent: 2002/0168100 (2002-11-01), Woodall
Chua,L.O. et al. “Cellular Neural Networks: Applications”, IEEE Trans. on Circuits and Systems, vol. 35, No. 10, pp. 1273-1290, IEEE, Oct. 1988.
Guzelis, C. et al. “Recurrent perceptron learning algorithm for completely stable cellular neural networks”, Proc. of the 3rd IEEE International Workshop on Cellular Neural Networks and Their Applications (CNNA-94), pp. 177-182, IEEE, Dec. 1994.

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